Lead LLM Engineer in Chester

Lead LLM Engineer in Chester

Chester Full-Time 185000 - 185000 £ / year (est.) Working from home possible
T

At a Glance

  • Tasks: Lead AI projects, optimise systems, and write production code in a hands-on role.
  • Company: Fast-scaling AI business with a global remote team and engineering-led culture.
  • Benefits: Up to £185K salary, £60K equity, fully remote work, and direct access to the CTO.
  • Other info: No CV needed; apply with your LinkedIn profile for a quick start.
  • Why this job: Join a dynamic team solving complex AI challenges and make a real impact.
  • Qualifications: Strong Python/PyTorch experience and knowledge of production-scale ML/LLM systems.

The predicted salary is between 185000 - 185000 £ per year.

I'm working on a unique AI role with one of the fastest-scaling AI businesses in the world right now. Up to £185,000 base + roughly £60,000 equity, fully remote globally. There is no office. Around 80 people worldwide. The company has scaled from 0 to 50 million users in around 2 years and is now processing 3 BILLION LLM tokens daily across mostly self-hosted infrastructure.

This is not an “AI wrapper” business. The engineering challenges are difficult:

  • inference optimisation
  • latency at scale
  • RAG/memory systems
  • RLHF/fine-tuning
  • moderation/alignment systems

They’re looking for a very hands-on AI Tech Lead who still enjoys building systems and writing production code. Strong experience with Python/PyTorch, vLLM, Hugging Face and production-scale ML/LLM systems is essential.

The sort of person likely to fit this role:

  • has shipped AI products used by millions
  • understands production AI systems at scale
  • values shipping quickly and pragmatically
  • enjoys ownership and autonomy

Small senior AI team, direct access to the CTO, low bureaucracy and a very engineering-led culture. Most people in the business have come from very successful startups or Tier 1 companies like Palantir, Meta and Anthropic, or companies with an outstanding engineering pedigree like Deel.

This role is open to anyone across the EU, and the company will pay in your local currency. For the ease of my network, the role is advertised in pounds, but the same salary would be paid out in euros etc. £185K is roughly €215K.

No CV needed at this stage. Feel free to apply with your LinkedIn profile and we can cross the CV bridge later.

Lead LLM Engineer in Chester employer: Tact

Join one of the fastest-scaling AI businesses globally, where you can thrive in a fully remote environment with a competitive salary of up to £185K plus equity. With a strong engineering-led culture and minimal bureaucracy, you'll have direct access to the CTO and the opportunity to work alongside a talented team from top-tier companies, fostering both personal and professional growth in a dynamic and innovative setting.

T

Contact Details:

Tact Recruitment Team

StudySmarter Expert Advice🤫

We think this is how you could land Lead LLM Engineer in Chester

Tip Number 1

Make sure your LinkedIn profile is up to date and showcases your skills in Python, PyTorch, and LLM systems. Highlight any projects where you've shipped AI products used by millions; this will grab attention!

Tip Number 2

Network like a pro! Reach out to connections in the AI field or those who work at companies you admire. A friendly chat can lead to referrals or insider tips that could help us land that dream role.

Tip Number 3

Prepare for interviews by brushing up on inference optimisation and latency at scale. We want to show that we can tackle the engineering challenges head-on and are ready to dive into hands-on coding.

Tip Number 4

Don’t forget to apply through our website! It’s a straightforward process, and it shows that you’re keen on joining the team. Plus, we can skip the CV step for now, making it even easier for us to get started.

We think you need these skills to ace Lead LLM Engineer in Chester

Python
PyTorch
vLLM
Hugging Face
Production-scale ML/LLM systems
Inference Optimisation
Latency at Scale

Some tips for your application 🫡

Show Your Passion for AI:When you're writing your application, let your enthusiasm for AI shine through! We want to see how your experience aligns with the challenges we face, like inference optimisation and RLHF. Make it personal and relatable!

Highlight Relevant Experience:Focus on your hands-on experience with Python, PyTorch, and production-scale ML systems. We’re looking for someone who has shipped AI products used by millions, so don’t be shy about showcasing your achievements!

Keep It Concise and Clear:We appreciate clarity! Keep your application straightforward and to the point. Avoid jargon unless it’s relevant to the role. Remember, we want to understand your skills and experiences quickly.

Apply Through Our Website:Don’t forget to apply through our website! It’s the easiest way for us to keep track of your application. Plus, you can use your LinkedIn profile, so no need to stress about a CV just yet!

How to prepare for a job interview at Tact

Know Your Tech Inside Out

Make sure you’re well-versed in Python, PyTorch, and the other technologies mentioned in the job description. Brush up on your knowledge of LLM systems and be ready to discuss specific projects where you've implemented these technologies.

Showcase Your Problem-Solving Skills

Prepare to talk about the engineering challenges you've faced, especially around inference optimisation and latency at scale. Use concrete examples to demonstrate how you tackled these issues and what the outcomes were.

Emphasise Your Hands-On Experience

This role is all about being hands-on, so highlight your experience in building systems and writing production code. Share stories that illustrate your ownership and autonomy in previous roles, as this aligns with the company culture.

Be Ready for a Technical Deep Dive

Expect technical questions that dive deep into your understanding of RAG/memory systems and RLHF/fine-tuning. Prepare to explain your thought process and decision-making in these areas, as it will show your depth of knowledge and practical experience.